Analyzing the Sentiment of Crowd for Improving the Emergency Response Services

Singh, Neha; Roy, Nirmalya; Gangopadhyay, Aryya

doi:10.1109/smartcomp.2018.00067

Cited by 19 publications

(9 citation statements)

References 23 publications

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“…Specific to the analysis of public emotion during social crises, Reference [ 27 ] used an emotional change detection approach to analyze users’ emotions on Twitter in the community involved in the Las Vegas shooting (October 2017). They argued that such analysis could help to improve emergency response services.…”

Section: Literature Reviewmentioning

confidence: 99%

Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs

Wei

Dang

et al. 2020

IJERPH

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Objective: Coronavirus disease 2019 (COVID-19) has caused substantial panic worldwide since its outbreak in December 2019. This study uses social networks to track the evolution of public emotion during COVID-19 in China and analyzes the root causes of these public emotions from an event-driven perspective. Methods: A dataset was constructed using microblogs (n = 125,672) labeled with COVID-19-related super topics (n = 680) from 40,891 users from 1 December 2019 to 17 February 2020. Based on the skeleton and key change points of COVID-19 extracted from microblogging contents, we tracked the public’s emotional evolution modes (accumulated emotions, emotion covariances, and emotion transitions) by time phase and further extracted the details of dominant social events. Results: Public emotions showed different evolution modes during different phases of COVID-19. Events about the development of COVID-19 remained hot, but generally declined, and public attention shifted to other aspects of the epidemic (e.g., encouragement, support, and treatment). Conclusions: These findings suggest that the public’s feedback on COVID-19 predated official accounts on the microblog platform. There were clear differences in the trending events that large users (users with many fans and readings) and common users paid attention to during each phase of COVID-19.

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Section: Literature Reviewmentioning

confidence: 99%

Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs

Wei

Dang

et al. 2020

IJERPH

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“…In this lexicon, a collection of words in English is associated with basic emotions and sentiments. The NRC lexicon has found application in earlier research for the classification of emotions in different contexts and varied domains [35,41,49,50]. The emotional classification of tweets in this study was done using a software package-the Syuzhet package in R [51].…”

Section: Classification Of Emotionsmentioning

confidence: 99%

“…Similarly, spatio-temporal analysis of clusters during a disease outbreak has helped in the identification of hotspots, areas of outbreak and the population at risk [37][38][39]. This data has helped the concerned management teams to undertake emergency public health interventions [40,41]. If data on negative emotions of people can be gathered along with their time stamps, in addition to their geographic location, it provides an insight into the spatial clusters that experience extreme negative emotions and the specific time of such occurrence.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015: a post hoc analysis

Karmegam

Mappillairaju

2020

Int J Health Geogr

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Background: Natural disasters are known to take their psychological toll immediately, and over the long term, on those living through them. Messages posted on Twitter provide an insight into the state of mind of citizens affected by such disasters and provide useful data on the emotional impact on groups of people. In 2015, Chennai, the capital city of Tamil Nadu state in southern India, experienced unprecedented flooding, which subsequently triggered economic losses and had considerable psychological impact on citizens. The objectives of this study are to (i) mine posts to Twitter to extract negative emotions of those posting tweets before, during and after the floods; (ii) examine the spatial and temporal variations of negative emotions across Chennai city via tweets; and (iii) analyse associations in the posts between the emotions observed before, during and after the disaster. Methods: Using Twitter's application programming interface, tweets posted at the time of floods were aggregated for detailed categorisation and analysis. The different emotions were extracted and classified by using the National Research Council emotion lexicon. Both an analysis of variance (ANOVA) and mixed-effect analysis were performed to assess the temporal variations in negative emotion rates. Global and local Moran's I statistic were used to understand the spatial distribution and clusters of negative emotions across the Chennai region. Spatial regression was used to analyse over time the association in negative emotion rates from the tweets. Results: In the 5696 tweets analysed around the time of the floods, negative emotions were in evidence 17.02% before, 29.45% during and 11.39% after the floods. The rates of negative emotions showed significant variation between tweets sent before, during and after the disaster. Negative emotions were highest at the time of disaster's peak and reduced considerably post disaster in all wards of Chennai. Spatial clusters of wards with high negative emotion rates were identified. Conclusions: Spatial analysis of emotions expressed on Twitter during disasters helps to identify geographic areas with high negative emotions and areas needing immediate emotional support. Analysing emotions temporally provides insight into early identification of mental health issues, and their consequences, for those affected by disasters.

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“…Neha Singh et.al [11], proposed an emergency response services by analyzing the sentiment of twitter users. Twitter API [9] was used to collect the data from twitter about the event happened in Las Vegas by using specific keywords such as #Lasvegas, #lasvegasshoot and applying the Naïve Bayes classifier to predict and classify eight different emotions.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method to Evaluate Students Sentiments from Twitter Messages

Sharma*¹,

Mansotra²

2019

IJRTE

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Classroom education is a dynamic environment, which brings students from different backgrounds with diverse abilities. By introducing machine-learning algorithms to learn the sentiments of the students in a classroom-based environment, can provide us a better research tool to understand the student psychology behind their attentiveness as well as the impact the instructor has on them while delivering lectures. Emotions can be analyzed mainly through many ways like facial features, audio signals and text messages. In this study, we have proposed a student emotion classifying mechanism that works after the lecture by analyzing the tweets posted by the students in the social media platform, Twitter to study their sentiments, or thoughts as expressed in the department twitter handle as a feedback to the classroom lecture. Students can post a tweet to their respective department’s twitter handle about their opinions, emotions, suggestion. Our application has been designed to monitor the department’s handle, a unique user-id via twitter API handler and when any posts appear, collects it and predicts the emotion. A hybrid-based approach which contains lexical and learning based approaches will be used to handle the twitter-based data and to predict the emotions of a student. A lexicon dictionary will be used in lexical based approach and for learning based approach, a manually customized dataset was used, and a support vector machine was designed to train the datasets and classify the emotions. The use-case of this application can be ideal for colleges, companies and wherever anyone wants to ease up the process of analyzing the feedback, suggestions or complaints from the students or employees, thereby saving considerable manpower and time. Our proposal is expected to garner good results and improved prediction time and accuracy.

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Analyzing the Sentiment of Crowd for Improving the Emergency Response Services

Cited by 19 publications

References 23 publications

Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs

Tracking and Analyzing Public Emotion Evolutions During COVID-19: A Case Study from the Event-Driven Perspective on Microblogs

Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015: a post hoc analysis

A Novel Method to Evaluate Students Sentiments from Twitter Messages

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